A Clinical Nurse Specialist Intervention to Facilitate Safe Transfer From ICU
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purpose of this article was to describe an innovative quality initiative implemented by the clinical nurses specialist in medicine to facilitate the transition process between the intensive care unit and the medical wards. BACKGROUND/RATIONALE: Safely transferring patients with complex health conditions from an area of high technology and increased monitoring, like the intensive care unit, to an area with lower nurse-to-patient ratio is an intricate process. The care of these patients, once transferred, also requires varying levels of expertise. As indicated in the nursing literature, this type of transition is often associated with high stress levels for the patient and family, as well as for the healthcare providers. To maximize patient safety and ensure optimal care for this patient population, well-defined mechanisms must be put in place. DESCRIPTION OF THE PROJECT/INNOVATION: The introduction of a formal assessment, consultation, and follow-up process conducted by a clinical nurse specialist (CNS). OUTCOMES: On average, 150 patients are assessed each year by the CNS. Among these patients, 15% are considered at high risk for complications upon transfer to the unit. INTERPRETATION/CONCLUSION/IMPLICATIONS: A systematic evaluation of patients by the CNS, before their transfer from the ICU to a medical unit, has been proven beneficial in ensuring a comprehensive patient care plan. Patients and families have verbalized that this intervention is helpful. Staff members have indicated that this safety initiative is useful in planning patient transfers. The next step would be to formally measure patient, family, and staff satisfaction with this initiative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".